Explanation: https://link.springer.com/chapter/10.1007/978-3-030-29736-7_5
| Student | Session | KC | Try | Time | Feedback | Correct | Opportunity | Gap | Gap_Type | Group | Cycle | Session_Complete | Complete_Plays | SRL | Time.Scale | Prev_Success | Prev_Failure | Age | Experience | Cadre | Level | ETAT | Region | Correct_First_Try |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| S1 | 2019-05-07 11:06:08 | KC1 | 1 | 32.122 | Essential | 0 | 0 | -4.60517 | None | Control | 1 | 0 | 0 | NA | 1.076694 | 0 | 0 | NA | NA | NA | NA | True | Western Asia | 0 |
| S1 | 2019-05-07 11:06:08 | KC1 | 2 | 11.688 | None | 1 | 1 | -4.60517 | None | Control | 1 | 0 | 0 | NA | -0.213432 | 0 | 1 | NA | NA | NA | NA | True | Western Asia | 0 |
| S2 | 2019-04-07 13:09:03 | KC1 | 1 | 39.471 | None | 1 | 0 | -4.60517 | None | Experiment | 1 | 0 | 2 | NA | 1.540682 | 0 | 0 | NA | NA | NA | NA | False | Sub-Saharan Africa | 1 |
| S2 | 2019-04-07 13:09:03 | KC2 | 1 | 91.466 | Reflective | 0 | 0 | -4.60517 | None | Experiment | 1 | 0 | 2 | NA | 4.823450 | 0 | 0 | NA | NA | NA | NA | False | Sub-Saharan Africa | 0 |
| S2 | 2019-04-07 13:09:03 | KC2 | 2 | 36.273 | Detailed | 0 | 1 | -4.60517 | None | Experiment | 1 | 0 | 2 | NA | 1.338772 | 0 | 1 | NA | NA | NA | NA | False | Sub-Saharan Africa | 0 |
| S2 | 2019-04-07 13:09:03 | KC2 | 3 | 19.065 | None | 1 | 2 | -4.60517 | None | Experiment | 1 | 0 | 2 | NA | 0.252324 | 0 | 2 | NA | NA | NA | NA | False | Sub-Saharan Africa | 0 |
| Levels | Incomplete | Complete | All |
|---|---|---|---|
| None | 1541 (13.66%) | 9743 (86.34%) | 11284 (60.21%) |
| Essential | 622 (26.49%) | 1726 (73.51%) | 2348 (12.53%) |
| Reflective | 264 (23.51%) | 859 (76.49%) | 1123 (5.99%) |
| Detailed | 1353 (33.95%) | 2632 (66.05%) | 3985 (21.26%) |
| Levels | Incomplete | Complete | All |
|---|---|---|---|
| None | 256 (44.76%) | 316 (55.24%) | 572 (37.91%) |
| <= 1 Hour | 156 (30%) | 364 (70%) | 520 (34.46%) |
| <= 1 Day | 52 (28.42%) | 131 (71.58%) | 183 (12.13%) |
| <= 1 Week | 37 (30.83%) | 83 (69.17%) | 120 (7.95%) |
| <= 1 Month | 27 (36.49%) | 47 (63.51%) | 74 (4.9%) |
| > 1 Month | 21 (52.5%) | 19 (47.5%) | 40 (2.65%) |
69.93% of learners had at least one complete learning session
The Bayesian Knowledge Tracing (BKT) model assumes the latent states to be independence among the difference skills in the learning module. Even within an individual skill, BKT assumes the probability of the next learning outcome to depend only on the latest previous outcome (The Markov assumption). Additionally BKT assumes that:
To address some of the shortcomings of BKT models, Learning Factors Analysis (LFA) and their different modalities such as Performance Factors Models (PFMs) and Additive Factor Models(AFMs) have been proposed (Cen et al., 2006, Cen et al., 2008, Pavlik Jr et al., 2009). They model student knowledge states using logistic regression models in order to deal with the incorporation of multiple skills while estimating student ability into the model. They exploit the number of successes or failures of a learner’s attempt at a KC to predict whether the learner has acquired understanding about the KC.
More details on the application of these models for this dataset can be found here: https://link.springer.com/chapter/10.1007/978-3-030-29736-7_5
AFM Accuracy: 0.689 (0.674 - 0.696) AUC: 0.748 (0.737 - 0.764)
PFM Accuracy: 0.767 (0.751 - 0.776) AUC: 0.85 (0.841 - 0.856)
The ‘Student’ variable accounts for 27.01% of the stochastic variation in the PFM model
L. Zeger, K. Y. Liang, and P. S. Albert. Models for longitudinal data: a generalized estimating equation approach. Biometrics, 44: 1049-1060 1988